Big Data

In today’s digital age, businesses require instant access to real-time customer data. However, even after investing heavily in CRM systems, service platforms, and analytics, businesses have to deal with fragmented data, disjointed processes, wasted spend and lost revenue. This is where Salesforce Data Cloud Implementation comes to the rescue. By unifying disparate data sources into a single one, this innovative solution drives customer engagement, boosts sales and drives efficiency. However, the true value comes from how this cloud platform must be implemented and the use cases that deliver business impact at scale. What Does Salesforce Data Cloud Actually Do? Salesforce Data Cloud collates data from various Salesforce applications, mobile apps, websites, data warehouses, call centers, and more into a unified customer profile. Unlike conventional data lakes, Data Cloud is implanted in Salesforce, which means a single source of customer data is available across all the Cloud platforms. Profiles updates occur uninterruptedly; AI models and automations can immediately act on the data, and insights don’t just sit in reports. Rather, they drive real-time actions throughout the customer journey. In other words, Data Cloud transforms scattered data into actionable intelligence that influences customer interaction. Why Most Data Cloud Projects Usually Fail? Despite its worth, several organizations fail to make the most of Salesforce Data Cloud for Enterprises as they approach it with restricted vision. Rather than leveraging it as an intelligence platform across the enterprise, most organizations use it only as a marketing tool, a database, or a Salesforce data cloud integration project. This approach leads to weak adoption, disconnected initiatives, and an ROI much lower than the true potential of Data Cloud. Salesforce Data Cloud Use Cases that Scale Real-Time Lead Intelligence for Sales Most sales teams rely blindly on CRM records that miss critical signals like website activity, usage of product, email engagement, support tickets, marketing communications, and business behavior. Salesforce Data Cloud brings all of these touchpoints into a continuously updated customer profile. For instance, when a prospect visits your pricing page numerous times, attends a webinar, and immediately has an open support ticket and an forthcoming renewal, Data Cloud instantly unifies this activity and surfaces it inside Sales Cloud, Einstein scoring, and lead and account records. This offers sales reps a clear view of purchasing intent, risk factors, level of engagement and upsell opportunities in one place. Smarter and Faster Customer Support Customer support teams are usually last in the line to get access to insightful customer data, though they rely on it the most. Salesforce Data Cloud provides agents a real-time view of every client, as soon as a conversation begins. When a client reaches out, the agent can see their buying history, previous interactions across various channels, subscriptions, marketing assignation, loyalty position, and product usage. This allows them to move right into solving the real issue rather than asking basic questions. At the same time, Einstein AI leverages this data to forecast risk of churn, suggest next-best actions, and suggest upsell offers in the flow of service. Since Data Cloud acts as the intelligence platform behind the entire operation — it enables quick resolutions, tailored support, and better outcomes at scale. Revenue Growth Via Cross-Sell and Upsell Most organizations have unexploited revenue within their present customer base. However, they lack the insight to identify who and when to target. Data Cloud unifies buying history, product usage, client lifecycle stage, support communications, and appointment data into a real-time view. It then identifies by default customers ready for upgrades, accounts that require other products, and users who are not fully utilizing their licenses. These segments flow directly into clouds, Agentforce or Einstein automations, enabling teams to act on openings rather than searching for them. Since the segments endlessly update as customer behavior changes, this approach scales way beyond static campaigns and constantly drives higher revenue. Personalization Beyond Marketing For many personalization translates to something as simple as an email subject line. However, true personalization rests on behavioral data that moves across every customer touchpoint. This becomes possible by Salesforce data cloud that links actions like browsing a product, abandoning a cart, and opening a mobile app into a unified customer profile. With this shared source of truth, all the cloud platforms work from the same live data. This would enable a customer to use the email received as a reference to what they just viewed, the support agent can view their abandoned cart, the website can showcase a relevant offer, and the mobile app can instantly update. Since the data model is used across all Salesforce clouds, personalization can scale without maintaining distinct engines for each channel. Einstein and Agentforce for AI-Powered Decision Making AI is powered by the data that backs it, and Salesforce Data Cloud makes Salesforce AI truly operative. By unifying actual customer behavior across systems, Data Cloud allows Einstein and Agentforce to create tailored emails, endorse next-best actions for teams, predict churn, lifetime value, the chances of conversion, and automate workflows using updated data. Without Data Cloud, AI is confined to fragmented CRM records. And since the intelligence layer grows like other systems such as product usage, billing and support, the AI becomes more accurate inevitably, enabling decision-making to scale across the complete organization. How to Implement Salesforce Data Cloud? Begin with the Outcome Success with Data Cloud relies on strategy rather than on software. High-performing teams begin their journey with a clear, outcome-driven roadmap, defining three to five experience-focused use cases before any data is connected. This ensures every integration supports quantifiable business impact. Connect What You Need Make sure to connect just the data that right away supports your priority use cases. Make sure to focus on the sources that will instantly drive the outcomes you care about most. Create an Integrated Data Model Make sure to align products, accounts, discourses into a single model. This lays the foundation that enables Data Cloud to deliver insights throughout the business. Activate Within Salesforce Data generates value when it is used. If data isn’t driving any value, it’s simply unused potential. Expand Across Teams Once your key use cases are up and running, Data Cloud should be scaled across various channels, regions and products to burgeon its impact across the organization. Final Words Salesforce Data Cloud converts raw data into actionable insights. It empowers business heads to turn every client interaction into an instant of intuition, engagement, and revenue. Organizations that put their data to work across the entire customer journey will be at an advantage. So, if you are considering implementing this innovative platform then you must consider availing Salesforce Data Cloud Implementation Services.

The business ecosystem has become extremely complex. To sustain themselves in a competitive landscape, it becomes extremely important for retailers to keep growing, innovating and diversifying. This would require them to understand and fulfill customer demands while provide them with a superior customer experience. Unfortunately, the retail marketplace is riddled with slow economic growth and high costs. The problem is further compounded by new age customers who seek personalization, and promotion based pricing, which has made it extremely difficult for retailers to sustain let alone expand and attract new customers. However, disruptive trends associated with retailers, as well as customers have resulted in massive explosion of data, which when processed and analyzed can provide retailers with a sizeable opportunity to understand customer behavior and other valuable insights that can be leveraged for informed decision making. However, processing such humongous and voluminous sets of data has become a tremendous challenge for companies, and will continue to grow in the years to come. According to report shared by Gartner, data volume is set to grow by 800% in the years to come, 80% of which will reside as unstructured data. To make sense of this wealth of big data, retailers require a robust data management solution that can help them retrieve and process data from multiple places. This will help them draw real-time insights for quick decision making while generate true business value. Predictive analytics has emerged as a sure shot solution to all the data woes faced by businesses. By anticipating customer needs based on post-interaction, historical and real-time analysis of big data, customer requirement can be fulfilled in a quick and efficient way. According to statistics shared by Forbes, there has been a whopping increase in number of companies adopting predictive analytics i.e. from a mere 17% in 2015 to a massive 53% in 2017. A combination of machine learning (ML) and artificial intelligence (AI), predictive analytics is growing all the more accurate and insightful. However, several businesses continue to assess this technology with certain degree of skepticism considering it to be too complex, disruptive and expensive to incorporate.  Listed below are few ways how businesses could achieve sales goals and increased customer satisfaction by leveraging this smart technology: Uncover Qualified Leads: Though, the entire practice of lead scoring has been in place since quite some time, they were largely based on guesswork and were extremely time-consuming. With a predictive analytics solution in place, businesses can make their sales and marketing teams more efficient by allowing them to access information such as whether your offering matches a potential customer’s needs, how convinced is your potential client to make a purchase, and similar other details that too in a precise way. This would allow your marketing and sales teams to better determine whether or not a customer is likely to convert and if yes what would be their potential lifetime value. This helps your sales team focus their efforts on the most rewarding areas. Measure Call Outcomes: Till date, many companies rely on manual assessment of data in order to determine the success of a new product campaign or sales strategy. Since, data is manually recorded, it is subjective and prone to inaccuracy. This doesn’t leave much scope for precisely evaluating campaign success. With an automated system in place, calls and activity are appropriately tracked. This allows the sales team of a business to accurately measure their sales performance. Reduce Customer Churn: Predictive analytics is extremely useful in identifying issues and trends that has a large impact on your business operation. This would help you predict when and why customers might consider abandoning you. Such important details will allow businesses to take proactive action to enhance customer experience and serve their needs in a better way. The altering demands of technological forces have influenced the retail sector in the biggest possible way. Predictive analytics has the potential to transform businesses and industries in a way that will make them more productive, competitive and efficient. Used effectively, this technology can turn out to be the key to your outbound sales success and customer satisfaction. In a nutshell, it will act as a magic bullet that may lead to improved profits and better business outcome. As a reputed name in the IT space, Girikon offers robust

Main point: Billions of rows X millions of columns Key Features: Modeled after Google’s BigTable Uses Hadoop’s HDFS as storage Map/reduce with Hadoop Query predicate push down via server side scan and get filters Optimizations for real time queries A high performance Thrift gateway HTTP supports XML, Protobuf, and binary Jruby-based (JIRB) shell Rolling restart for configuration changes and minor upgrades Random access performance is like MySQL A cluster consists of several different types of nodes Best used: Hadoop is probably still the best way to run Map/Reduce jobs on huge datasets. Best if you use the Hadoop/HDFS stack already. Examples: Search engines. Analysing log data. Any place where scanning huge, two-dimensional join-less tables are a requirement. Regards, Alok In case you need Certified Salesforce Consultant for any Salesforce related work, then please feel free to reach out to sales@girikon.com

Ideally Big Data is term for large and huge amount of data. Now a day’s data piles are growing exponentially. This data is coming from various sources like call logs, web logs, digital transactions, social media posts, sensors data & log, pictures, videos and everything which is digital is contributing. Whereas Big Data doesn’t specifically indicates to any size or quantity, yet it is referred when we talk for data of petabytes and exabytes. Now Big Data is an evolving & popular term and in the current age main challenge with this plenty amount of data is how to manage it and how to get productive information from here. There are three prime factors of Big Data: 1. Volume : Analytics on massive amount of data 2. Velocity : Faster & robust transactions with uninterrupted availability 3. Variety : Wide variety of data from different scenarios. Where our traditional techniques are inadequate to process high volume of data , Big Data makes your business more agile, flexible and swift and to convert potential data into useful information. Dealing with larger datasets it help us to manage both structured semi-structured or un-structured data. Because traditional applications or databases takes too much time to load voluminous data and obviously costs too much, new approaches use complex algorithms for the same thing which reduces time and cost both. In such mechanism main focus is on mining for information rather than emphasizing on data schema and data quality. Following are few references of that technologies which born to handle this buzzword “Big Data”. Cassandra DB, MongoDB, HBase, ElasticSearch, Apache Cassandra etc Cheers! Pramod In case you need Certified Salesforce Consultant for any Salesforce related work, then please feel free to reach out to sales@girikon.com

Main Part: Store huge datasets in “almost” SQL Key Features of Cassandra DB: Querying by key, or key range (secondary indices are also available) Data can have expiration (set on INSERT) Writes can be much faster than reads (when reads are disk-bound) Map/reduce possible with Apache Hadoop All nodes are similar, as opposed to Hadoop/HBase Very good and reliable cross-datacenter replication Distributed counter data type You can write triggers in Java Best use: When you need to store data so huge that it doesn’t fit on server, but still want a friendly familiar interface to it. Examples: Web analytics, to count hits by hour, by browser, by IP, etc. Transaction logging. Data collection from huge sensor arrays. Regards, Alok